{
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  "metadata": {
    "colab": {
      "name": "0503.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yananma/5_programs_per_day/blob/master/0503.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V1-WziwijVkb",
        "colab_type": "text"
      },
      "source": [
        "## 3.8 多层感知机"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_ITeUupj8-F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install mxnet d2lzh"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NxdL3TUkWfQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline \n",
        "import d2lzh as d2l \n",
        "from mxnet import autograd, nd \n",
        "\n",
        "def xyplot(x_vals, y_vals, name):\n",
        "    d2l.set_figsize(figsize=(5, 2.5))\n",
        "    d2l.plt.plot(x_vals.asnumpy(), y_vals.asnumpy())\n",
        "    d2l.plt.xlabel('x')\n",
        "    d2l.plt.ylabel(name + '(x)')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPXTzldxnGNK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "82a040f3-ebdb-43fd-a3a0-f02091d7067e"
      },
      "source": [
        "x = nd.arange(-8.0, 8.0, 0.1)\n",
        "x.attach_grad()\n",
        "with autograd.record():\n",
        "    y = x.relu()\n",
        "xyplot(x, y, 'relu')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x180 with 1 Axes>"
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          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lpev7UNepaCX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "2ba72fad-d344-4a9f-d380-4c02b06385e9"
      },
      "source": [
        "y.backward()\n",
        "xyplot(x, x.grad, 'grad of relu')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxXCcqrCqAqb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "cdc2addb-a3dc-42db-ed6b-c8afe9c843b8"
      },
      "source": [
        "with autograd.record():\n",
        "    y = x.sigmoid()\n",
        "xyplot(x, y, 'sigmoid')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yoXg7FeGqL6P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "9402f086-36b0-47d5-931d-9c5ab465526e"
      },
      "source": [
        "y.backward()\n",
        "xyplot(x, x.grad, 'grad of sigmoid')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pT5qMNkgqvRU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "5b909c9d-fabf-4f48-cbc8-2138ef2480f5"
      },
      "source": [
        "with autograd.record():\n",
        "    y = x.tanh()\n",
        "xyplot(x, y, 'tanh')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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    },
    {
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        "colab_type": "code",
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        "outputId": "f8a52495-6fcc-44c3-f4d1-a5eabdf3d3dd"
      },
      "source": [
        "y.backward()\n",
        "xyplot(x, x.grad, 'grad of tanh')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJKmY_PFrFHT",
        "colab_type": "text"
      },
      "source": [
        "## 3.9 多层感知机的从零开始实现"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJ4xOzpptoe6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline \n",
        "import d2lzh as d2l \n",
        "from mxnet import nd \n",
        "from mxnet.gluon import loss as gloss "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afWkDC4guNgz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "39f68209-3335-4934-f3f5-0fcc04512a3e"
      },
      "source": [
        "batch_size = 256 \n",
        "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading /root/.mxnet/datasets/fashion-mnist/train-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-images-idx3-ubyte.gz...\n",
            "Downloading /root/.mxnet/datasets/fashion-mnist/train-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz...\n",
            "Downloading /root/.mxnet/datasets/fashion-mnist/t10k-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/t10k-images-idx3-ubyte.gz...\n",
            "Downloading /root/.mxnet/datasets/fashion-mnist/t10k-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/t10k-labels-idx1-ubyte.gz...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jcgVy1_69l_X",
        "colab_type": "text"
      },
      "source": [
        "**Reinit**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmfUpHpRuXo2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_inputs, num_outputs, num_hiddens1, num_hiddens2 = 784, 10, 500, 390\n",
        "\n",
        "W1 = nd.random.normal(scale=0.01, shape=(num_inputs, num_hiddens1))\n",
        "b1 = nd.zeros(num_hiddens1)\n",
        "W2 = nd.random.normal(scale=0.01, shape=(num_hiddens1, num_hiddens2))\n",
        "b2 = nd.zeros(num_hiddens2)\n",
        "W3 = nd.random.normal(scale=0.01, shape=(num_hiddens2, num_outputs))\n",
        "b3 = nd.zeros(num_outputs)\n",
        "params = [W1, b1, W2, b2, W3, b3]\n",
        "\n",
        "for param in params:\n",
        "    param.attach_grad()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plO9Ik5c4uc2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def relu(X):\n",
        "    return nd.maximum(X, 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9m1Ug3gI8jD4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def net(X):\n",
        "    X = X.reshape((-1, num_inputs))\n",
        "    H1 = relu(nd.dot(X, W1) + b1)\n",
        "    H2 = relu(nd.dot(H1, W2) + b2)\n",
        "    return nd.dot(H2, W3) + b3 "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGWDVi_g87zE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss = gloss.SoftmaxCrossEntropyLoss()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMImOLW--0Pp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate_accuracy(data_iter, net):\n",
        "    acc_sum, n = 0.0, 0 \n",
        "    for X, y in data_iter:\n",
        "        y = y.astype('float32')\n",
        "        acc_sum += (net(X).argmax(axis=1) == y).sum().asscalar()\n",
        "        n += y.size \n",
        "    return acc_sum / n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqiWuUQ-9Aoq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time \n",
        "\n",
        "num_epochs, lr = 5, 0.5 \n",
        "\n",
        "train_ls, train_acc, test_acc_plot = [], [], []\n",
        "def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params=None, lr=None, trainer=None):\n",
        "    for epoch in range(num_epochs):\n",
        "        train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()\n",
        "        for X, y in train_iter:\n",
        "            with autograd.record():\n",
        "                y_hat = net(X)\n",
        "                l = loss(y_hat, y).sum()\n",
        "            l.backward()\n",
        "            if trainer is None:\n",
        "                d2l.sgd(params, lr, batch_size)\n",
        "            else:\n",
        "                trainer.step(batch_size)\n",
        "            y = y.astype('float32')\n",
        "            train_l_sum += l.asscalar()\n",
        "            train_acc_sum += (y_hat.argmax(axis=1) == y).sum().asscalar()\n",
        "            n += y.size \n",
        "            train_ls.append((l / y.size).asscalar())\n",
        "            train_acc.append((y_hat.argmax(axis=1) == y).mean().asscalar())\n",
        "        test_acc = evaluate_accuracy(test_iter, net)\n",
        "        test_acc_plot.append(test_acc)\n",
        "        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec' \n",
        "           % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc, time.time() - start))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UOb_Hm99Vhw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "57d96a3a-c58e-49a0-a205-f2ad14b2dce3"
      },
      "source": [
        "train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, loss 1.2301, train acc 0.538, test acc 0.784, time 14.0 sec\n",
            "epoch 2, loss 0.5558, train acc 0.791, test acc 0.834, time 14.3 sec\n",
            "epoch 3, loss 0.4563, train acc 0.831, test acc 0.855, time 14.2 sec\n",
            "epoch 4, loss 0.4200, train acc 0.844, test acc 0.863, time 14.1 sec\n",
            "epoch 5, loss 0.3914, train acc 0.855, test acc 0.865, time 13.9 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VqcLLILx9hnf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "82cdd804-2763-43f8-88ee-9229a308deb7"
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "\n",
        "plt.plot(train_ls, label='train loss sum')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
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94.112725 193.247918 \nL 94.371978 193.611866 \nL 94.631232 190.538129 \nL 94.890485 191.088911 \nL 95.408992 201.918719 \nL 95.927499 204.33273 \nL 96.186753 201.145767 \nL 96.446006 194.106272 \nL 96.70526 204.47577 \nL 97.48302 189.868331 \nL 98.001527 200.456596 \nL 98.260781 200.973631 \nL 98.520034 198.768509 \nL 98.779288 199.991743 \nL 99.038542 201.771759 \nL 99.297795 197.136318 \nL 99.557049 205.383311 \nL 99.816302 202.440669 \nL 100.075556 201.000974 \nL 100.334809 188.130069 \nL 100.594063 188.988439 \nL 101.371823 201.591529 \nL 101.631077 202.317288 \nL 101.89033 201.37676 \nL 102.149584 198.273321 \nL 102.408837 202.525396 \nL 102.668091 204.691271 \nL 103.186598 197.455239 \nL 103.445851 194.972488 \nL 103.705105 200.229077 \nL 103.964358 199.674208 \nL 104.223612 201.451156 \nL 104.482865 198.807096 \nL 104.742119 199.96718 \nL 105.001373 196.484857 \nL 105.260626 197.55436 \nL 105.51988 195.351666 \nL 106.29764 199.942488 \nL 106.816147 200.787544 \nL 107.334654 195.748274 \nL 107.593908 198.59799 \nL 107.853161 203.146512 \nL 108.112415 204.080557 \nL 108.371668 203.070628 \nL 108.630922 202.639045 \nL 108.890175 204.595941 \nL 109.149429 203.735051 \nL 109.408682 204.218218 \nL 109.667936 203.270918 \nL 109.927189 200.56517 \nL 110.186443 201.179058 \nL 110.445697 199.135414 \nL 110.70495 203.248981 \nL 110.964204 203.186471 \nL 111.482711 198.583093 \nL 111.741964 197.661003 \nL 112.001218 197.815652 \nL 112.260471 204.925426 \nL 112.519725 206.16213 \nL 112.778978 204.524323 \nL 113.038232 205.406003 \nL 113.297485 201.497113 \nL 113.556739 202.413173 \nL 113.815992 202.455708 \nL 114.075246 199.524926 \nL 114.334499 204.5876 \nL 114.593753 204.810465 \nL 114.853006 206.65764 \nL 115.11226 204.280359 \nL 115.371513 205.551271 \nL 115.630767 200.65244 \nL 115.890021 202.670876 \nL 116.149274 198.967968 \nL 116.667781 205.544183 \nL 117.186288 198.260528 \nL 117.445542 203.093846 \nL 117.704795 204.107036 \nL 117.964049 199.093552 \nL 118.223302 203.89424 \nL 118.482556 202.983133 \nL 118.741809 205.516665 \nL 119.001063 200.235031 \nL 119.260316 204.062867 \nL 119.51957 204.078866 \nL 119.778823 205.442736 \nL 120.038077 198.990953 \nL 120.29733 202.472935 \nL 120.556584 207.820436 \nL 120.815837 205.985842 \nL 121.075091 202.211035 \nL 121.334345 202.024783 \nL 121.593598 199.280493 \nL 121.852852 202.273921 \nL 122.112105 202.761615 \nL 122.371359 199.976208 \nL 122.630612 190.552546 \nL 122.889866 199.214322 \nL 123.408373 204.041448 \nL 123.667626 204.805994 \nL 123.92688 202.636684 \nL 124.186133 202.677273 \nL 124.445387 203.458618 \nL 124.70464 201.274787 \nL 124.963894 194.734379 \nL 125.223147 202.27737 \nL 125.482401 202.29227 \nL 125.741654 198.504586 \nL 126.000908 199.871201 \nL 126.519415 206.848645 \nL 126.778668 207.059685 \nL 127.037922 203.787577 \nL 127.297176 204.014562 \nL 127.556429 205.680055 \nL 128.074936 199.626584 \nL 128.33419 200.311042 \nL 128.593443 204.096513 \nL 128.852697 202.824418 \nL 129.11195 206.616669 \nL 129.371204 202.502416 \nL 129.630457 204.615149 \nL 129.889711 200.461633 \nL 130.148964 204.445706 \nL 130.408218 205.056784 \nL 130.667471 199.549284 \nL 130.926725 203.48352 \nL 131.185978 204.920683 \nL 131.445232 209.448266 \nL 131.704485 205.455218 \nL 131.963739 204.565579 \nL 132.222992 200.417268 \nL 132.482246 201.23278 \nL 132.7415 203.459101 \nL 133.000753 204.30989 \nL 133.260007 204.141905 \nL 133.51926 202.999156 \nL 133.778514 205.919568 \nL 134.037767 202.066035 \nL 134.297021 205.806996 \nL 134.556274 206.771567 \nL 134.815528 206.080395 \nL 135.074781 203.398834 \nL 135.334035 199.392176 \nL 135.852542 205.461198 \nL 136.111795 204.121807 \nL 136.371049 203.540878 \nL 136.630302 202.169143 \nL 136.889556 200.089632 \nL 137.148809 201.566476 \nL 137.408063 202.071748 \nL 137.667316 202.085021 \nL 137.92657 200.995577 \nL 138.185824 201.884737 \nL 138.445077 205.618373 \nL 138.704331 205.409204 \nL 138.963584 204.263097 \nL 139.222838 205.757497 \nL 139.482091 206.007756 \nL 139.741345 205.81102 \nL 140.000598 207.486304 \nL 140.259852 202.975786 \nL 140.519105 205.839019 \nL 140.778359 205.730449 \nL 141.037612 204.956872 \nL 141.296866 201.523461 \nL 141.556119 207.012337 \nL 141.815373 205.826447 \nL 142.074626 206.723864 \nL 142.33388 207.032371 \nL 142.593133 206.571683 \nL 142.852387 205.137723 \nL 143.11164 201.335511 \nL 143.370894 201.437763 \nL 143.630148 200.161629 \nL 143.889401 200.158257 \nL 144.148655 203.978228 \nL 144.407908 205.843354 \nL 144.667162 206.272684 \nL 144.926415 199.258619 \nL 145.444922 204.424695 \nL 145.704176 203.404684 \nL 146.222683 209.691065 \nL 146.74119 199.2513 \nL 147.000443 204.416409 \nL 147.259697 203.732311 \nL 147.51895 203.927993 \nL 147.778204 206.79674 \nL 148.037457 207.701117 \nL 148.296711 205.273961 \nL 148.815218 207.153005 \nL 149.074472 202.521803 \nL 149.333725 203.329601 \nL 149.592979 202.670563 \nL 149.852232 205.286957 \nL 150.111486 206.665921 \nL 150.370739 205.951453 \nL 150.629993 206.297395 \nL 150.889246 205.824183 \nL 151.1485 206.307203 \nL 151.407753 205.60583 \nL 151.667007 206.949793 \nL 151.92626 202.606973 \nL 152.185514 193.984392 \nL 152.444767 193.297473 \nL 152.963274 205.309497 \nL 153.222528 206.453903 \nL 153.481781 206.545359 \nL 153.741035 203.20108 \nL 154.000288 205.692957 \nL 154.259542 206.725572 \nL 154.518795 205.692696 \nL 155.037303 195.296009 \nL 155.296556 199.571277 \nL 155.55581 198.942323 \nL 155.815063 202.535965 \nL 156.074317 203.61697 \nL 156.592824 202.805511 \nL 156.852077 203.766289 \nL 157.111331 209.921801 \nL 157.370584 209.819144 \nL 157.629838 206.072746 \nL 157.889091 207.211829 \nL 158.148345 208.886457 \nL 158.666852 205.561763 \nL 158.926105 206.735879 \nL 159.185359 209.001441 \nL 159.444612 207.802926 \nL 159.703866 205.451877 \nL 159.963119 205.44494 \nL 160.222373 207.914601 \nL 160.481627 205.632939 \nL 160.74088 208.271515 \nL 161.259387 205.028914 \nL 161.518641 205.385923 \nL 161.777894 204.194466 \nL 162.037148 208.309003 \nL 162.296401 204.085099 \nL 162.555655 205.986335 \nL 163.074162 207.214271 \nL 163.333415 206.43509 \nL 163.592669 207.13025 \nL 163.851922 207.316859 \nL 164.111176 208.176627 \nL 164.370429 202.866287 \nL 164.629683 200.637018 \nL 164.888936 201.223833 \nL 165.14819 207.96637 \nL 165.407443 203.126308 \nL 165.666697 202.93775 \nL 165.925951 205.557964 \nL 166.185204 202.087886 \nL 166.444458 203.870889 \nL 166.703711 203.559306 \nL 166.962965 207.994175 \nL 167.222218 205.436324 \nL 167.481472 205.689456 \nL 167.740725 198.835128 \nL 167.999979 205.723769 \nL 168.259232 202.056612 \nL 168.518486 203.938051 \nL 168.777739 208.553448 \nL 169.036993 207.710314 \nL 169.296246 205.586457 \nL 169.5555 208.157263 \nL 169.814753 207.314094 \nL 170.074007 205.818911 \nL 170.33326 205.417574 \nL 170.592514 205.311367 \nL 170.851767 204.762732 \nL 171.111021 206.092345 \nL 171.370275 205.629457 \nL 171.629528 207.889107 \nL 171.888782 206.538985 \nL 172.148035 205.980817 \nL 172.407289 204.575025 \nL 172.666542 208.209809 \nL 172.925796 205.445369 \nL 173.185049 207.013278 \nL 173.703556 206.137113 \nL 174.222063 198.136811 \nL 174.481317 200.604802 \nL 174.74057 206.916555 \nL 174.999824 206.399792 \nL 175.259077 204.953363 \nL 175.518331 200.222749 \nL 175.777584 208.452936 \nL 176.296091 205.809254 \nL 176.555345 208.44536 \nL 176.814598 203.693112 \nL 177.073852 206.904919 \nL 177.333106 207.960687 \nL 177.851613 204.2448 \nL 178.110866 205.020646 \nL 178.37012 208.296917 \nL 178.629373 209.47869 \nL 179.14788 204.299172 \nL 179.407134 205.977348 \nL 179.666387 204.148278 \nL 179.925641 206.093819 \nL 180.184894 202.595516 \nL 180.444148 203.904028 \nL 180.703401 209.691382 \nL 181.221908 205.61724 \nL 181.481162 208.752099 \nL 181.740415 207.249539 \nL 182.258922 205.71237 \nL 182.518176 208.257535 \nL 182.77743 205.531386 \nL 183.036683 206.045309 \nL 183.295937 201.700229 \nL 183.55519 201.416318 \nL 183.814444 203.401859 \nL 184.073697 206.616533 \nL 184.332951 202.172052 \nL 184.592204 206.973951 \nL 184.851458 206.140576 \nL 185.110711 208.113554 \nL 185.369965 205.528053 \nL 185.629218 208.630261 \nL 185.888472 207.682136 \nL 186.147725 211.912517 \nL 186.406979 208.55272 \nL 186.666232 206.870804 \nL 186.925486 207.308436 \nL 187.184739 208.619731 \nL 187.443993 207.098755 \nL 187.703246 207.44748 \nL 187.9625 207.315331 \nL 188.221754 207.492395 \nL 188.481007 208.081183 \nL 188.740261 206.869692 \nL 188.999514 208.469882 \nL 189.258768 205.427901 \nL 189.518021 209.336103 \nL 189.777275 206.512282 \nL 190.036528 201.29368 \nL 190.295782 201.08097 \nL 190.555035 204.434647 \nL 190.814289 205.419699 \nL 191.073542 205.732833 \nL 191.332796 209.290334 \nL 191.851303 206.910576 \nL 192.110556 208.879945 \nL 192.36981 208.174126 \nL 192.629063 202.500798 \nL 192.888317 204.611686 \nL 193.14757 208.951128 \nL 193.406824 205.980777 \nL 193.666078 209.797704 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208.253215 \nL 205.850993 205.436142 \nL 206.110247 205.711273 \nL 206.3695 210.480335 \nL 206.628754 204.148172 \nL 206.888007 203.421595 \nL 207.147261 204.174613 \nL 207.406514 208.140393 \nL 207.665768 206.165689 \nL 207.925021 207.435287 \nL 208.184275 210.001842 \nL 208.443528 207.704878 \nL 208.702782 209.955614 \nL 208.962035 208.84848 \nL 209.221289 207.183739 \nL 209.480542 212.465953 \nL 209.739796 205.657288 \nL 209.999049 205.713206 \nL 210.258303 206.041818 \nL 210.77681 207.287821 \nL 211.036064 207.200777 \nL 211.295317 207.939045 \nL 211.554571 207.958265 \nL 211.813824 209.943943 \nL 212.073078 204.504305 \nL 212.332331 203.632464 \nL 212.591585 199.602403 \nL 213.110092 205.937981 \nL 213.369345 207.808036 \nL 213.628599 205.170883 \nL 213.887852 207.767949 \nL 214.147106 205.478129 \nL 214.406359 207.027124 \nL 214.665613 206.583398 \nL 214.924866 207.659853 \nL 215.18412 204.47076 \nL 215.443373 205.176936 \nL 215.702627 208.123252 \nL 215.961881 206.185414 \nL 216.221134 207.352096 \nL 216.480388 203.655246 \nL 216.739641 209.157475 \nL 216.998895 209.094548 \nL 217.258148 208.543787 \nL 217.517402 206.36048 \nL 218.035909 208.952192 \nL 218.295162 202.731933 \nL 218.554416 209.741293 \nL 218.813669 209.608546 \nL 219.072923 206.733863 \nL 219.332176 206.959379 \nL 219.59143 209.922577 \nL 219.850683 207.77312 \nL 220.109937 208.710219 \nL 220.36919 207.4761 \nL 220.628444 207.556271 \nL 220.887697 207.292521 \nL 221.146951 209.88426 \nL 221.665458 207.879195 \nL 221.924712 209.604081 \nL 222.183965 207.159014 \nL 222.443219 206.272692 \nL 222.702472 206.98549 \nL 222.961726 208.867031 \nL 223.220979 209.099892 \nL 223.480233 208.097998 \nL 223.739486 209.890284 \nL 223.99874 206.931757 \nL 224.257993 208.651441 \nL 224.517247 207.639219 \nL 224.7765 204.126312 \nL 225.035754 206.68745 \nL 225.295007 207.805455 \nL 225.554261 209.672618 \nL 225.813514 209.939295 \nL 226.072768 207.432925 \nL 226.332021 209.575488 \nL 226.591275 206.96559 \nL 226.850528 208.466235 \nL 227.109782 209.218297 \nL 227.628289 208.846614 \nL 227.887543 208.916275 \nL 228.146796 202.975005 \nL 228.40605 203.831309 \nL 228.665303 206.071349 \nL 228.924557 204.618555 \nL 229.18381 205.555028 \nL 229.443064 207.894975 \nL 229.702317 208.647957 \nL 229.961571 207.212738 \nL 230.220824 204.978923 \nL 230.480078 204.880653 \nL 230.739331 208.09751 \nL 230.998585 206.529672 \nL 231.257838 210.637291 \nL 231.517092 211.869514 \nL 231.776345 208.984004 \nL 232.035599 211.144064 \nL 232.294852 209.110803 \nL 232.554106 208.960449 \nL 232.81336 207.295899 \nL 233.072613 210.346497 \nL 233.331867 208.272922 \nL 233.59112 207.135786 \nL 233.850374 207.84914 \nL 234.109627 204.68656 \nL 234.368881 208.60097 \nL 234.628134 208.302395 \nL 234.887388 208.80564 \nL 235.146641 204.29117 \nL 235.405895 209.685144 \nL 235.665148 207.311163 \nL 235.924402 208.147158 \nL 236.183655 211.525324 \nL 236.442909 205.98329 \nL 236.702162 209.295995 \nL 237.220669 205.938414 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          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-GHQExn_WM4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "61e60ccf-8520-416b-9933-05dd262e986a"
      },
      "source": [
        "plt.plot(train_acc, label='train acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jC3mrBTL_Z2O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "ecf05ac0-fd24-4e85-ffc4-956b27af9379"
      },
      "source": [
        "plt.plot(test_acc_plot, label='test acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqTHW6Rde7yF",
        "colab_type": "text"
      },
      "source": [
        "## 3.10 多层感知机的简洁实现"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5joYOWeGfItA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import d2lzh as d2l \n",
        "from mxnet import gluon, init \n",
        "from mxnet.gluon import loss as gloss, nn "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3N4uH7xfRyk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = nn.Sequential()\n",
        "net.add(nn.Dense(256, activation='relu'), \n",
        "    nn.Dense(10))\n",
        "net.initialize(init.Normal(sigma=0.01))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZGCLyqCfhGO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "930973b7-9170-4643-f2c1-7c0fc48dc537"
      },
      "source": [
        "batch_size = 256\n",
        "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n",
        "\n",
        "loss = gloss.SoftmaxCrossEntropyLoss()\n",
        "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.5})\n",
        "num_epochs = 5 \n",
        "d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, trainer)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, loss 0.8332, train acc 0.690, test acc 0.827\n",
            "epoch 2, loss 0.4916, train acc 0.818, test acc 0.851\n",
            "epoch 3, loss 0.4276, train acc 0.841, test acc 0.859\n",
            "epoch 4, loss 0.3976, train acc 0.855, test acc 0.866\n",
            "epoch 5, loss 0.3760, train acc 0.861, test acc 0.864\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOJdilI6geRO",
        "colab_type": "text"
      },
      "source": [
        "## 3.11 模型选择、欠拟合和过拟合"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrsJOMa5jEy2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline \n",
        "import d2lzh as d2l \n",
        "from mxnet import autograd, gluon, nd \n",
        "from mxnet.gluon import data as gdata, loss as gloss "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44UvUKJAo_V3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_train, n_test, true_w, true_b = 100, 100, [1.2, -3.4, 5.6], 5 \n",
        "features = nd.random.normal(shape=(n_train + n_test, 1))\n",
        "poly_features = nd.concat(features, nd.power(features, 2), nd.power(features, 3))\n",
        "labels = (true_w[0] * poly_features[:, 0] + true_w[1] * poly_features[:, 1] + true_w[2] * poly_features[:, 2] + true_b)\n",
        "labels += nd.random.normal(scale=0.1, shape=labels.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVulBiUWqYls",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1531c45f-7af5-4532-8485-08f937d13d10"
      },
      "source": [
        "features.shape"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j1w_sr7BqbV2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5fc172f8-e1f0-4277-ba84-f1d3aeea47ee"
      },
      "source": [
        "poly_features.shape"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oci_sfg8qkqE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ebededab-c8fc-421a-dac2-9c0b1201a70f"
      },
      "source": [
        "labels.shape"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4S25bsId-N7V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "3c3dbbff-3321-4293-d36d-745861b24216"
      },
      "source": [
        "my_a = nd.array([2, 3])\n",
        "nd.power(my_a, 2)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[4. 9.]\n",
              "<NDArray 2 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QAWDBywAi6v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "2c9758cd-b427-4d4b-f6e7-1ae19e3a5892"
      },
      "source": [
        "features[:10]"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[-0.9790981 ]\n",
              " [ 1.0471004 ]\n",
              " [-0.85697544]\n",
              " [ 0.90210015]\n",
              " [ 1.0002568 ]\n",
              " [-2.2165654 ]\n",
              " [ 0.1425914 ]\n",
              " [ 0.24022633]\n",
              " [ 0.06352675]\n",
              " [-0.03986425]]\n",
              "<NDArray 10x1 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NA4FGZw0AmUN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "e0639cbd-cfba-4395-a826-964b9ca77246"
      },
      "source": [
        "poly_features[:10]"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[-9.7909808e-01  9.5863307e-01 -9.3859577e-01]\n",
              " [ 1.0471004e+00  1.0964193e+00  1.1480612e+00]\n",
              " [-8.5697544e-01  7.3440689e-01 -6.2936866e-01]\n",
              " [ 9.0210015e-01  8.1378466e-01  7.3411524e-01]\n",
              " [ 1.0002568e+00  1.0005137e+00  1.0007706e+00]\n",
              " [-2.2165654e+00  4.9131622e+00 -1.0890345e+01]\n",
              " [ 1.4259140e-01  2.0332308e-02  2.8992123e-03]\n",
              " [ 2.4022633e-01  5.7708688e-02  1.3863146e-02]\n",
              " [ 6.3526750e-02  4.0356480e-03  2.5637160e-04]\n",
              " [-3.9864253e-02  1.5891587e-03 -6.3350628e-05]]\n",
              "<NDArray 10x3 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4qHE8JWsApLp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "3676fc62-0796-4f00-b4d4-81f5e2a71cb6"
      },
      "source": [
        "labels[:10]"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[ -4.566543    9.086166   -2.0599427   7.552543    8.451743  -75.39401\n",
              "   5.135997    4.9987454   4.976468    4.936872 ]\n",
              "<NDArray 10 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVjHj_qRBDnt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def semilogy(x_vals, y_vals, x_label, y_label, x2_vals=None, y2_vals=None, legend=None, figsize=(3.5, 2.5)):\n",
        "    d2l.set_figsize(figsize)\n",
        "    d2l.plt.xlabel(x_label)\n",
        "    d2l.plt.ylabel(y_label)\n",
        "    d2l.plt.semilogy(x_vals, y_vals)\n",
        "    if x2_vals and y2_vals:\n",
        "        d2l.plt.semilogy(x2_vals, y2_vals, linestyle=':')\n",
        "        d2l.plt.legend(legend)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpCfJBjfCA1O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_epochs, loss = 100, gloss.L2Loss()\n",
        "\n",
        "def fit_and_plot(train_features, test_features, train_labels, test_labels):\n",
        "    net = nn.Sequential()\n",
        "    net.add(nn.Dense(1))\n",
        "    net.initialize()\n",
        "    batch_size = min(10, train_labels.shape[0])\n",
        "    train_iter = gdata.DataLoader(gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True)\n",
        "    trainer = gluon.Trainer(net.collect_params(), 'sgd', {\"learning_rate\": 0.01})\n",
        "    train_ls, test_ls = [], []\n",
        "    for _ in range(num_epochs):\n",
        "        for X, y in train_iter:\n",
        "            with autograd.record():\n",
        "                l = loss(net(X), y)\n",
        "            l.backward()\n",
        "            trainer.step(batch_size)\n",
        "        train_ls.append(loss(net(train_features), train_labels).mean().asscalar())\n",
        "        test_ls.append(loss(net(test_features), test_labels).mean().asscalar())\n",
        "    print('final epoch: train loss', train_ls[-1], 'test loss', test_ls[-1])\n",
        "    semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss', \n",
        "        range(1, num_epochs + 1), test_ls, ['train', 'test'])\n",
        "    print('weight:', net[0].weight.data().asnumpy(), \n",
        "        '\\nbias:', net[0].bias.data().asnumpy())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5-7XKp6Eyg_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "be23b598-0a97-465f-f814-2e6c69247716"
      },
      "source": [
        "fit_and_plot(poly_features[:n_train, :], poly_features[n_train:, :], \n",
        "             labels[:n_train], labels[n_train:])"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "final epoch: train loss 0.010790647 test loss 0.01197083\n",
            "weight: [[ 1.0693383 -3.3242254  5.640964 ]] \n",
            "bias: [4.929015]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
            ],
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    {
      "cell_type": "code",
      "metadata": {
        "id": "4U-65UpuFM06",
        "colab_type": "code",
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      },
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        "fit_and_plot(features[:n_train, :], features[n_train:, :], labels[:n_train], labels[n_train:])"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
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            "final epoch: train loss 186.08691 test loss 72.6864\n",
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    {
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        "fit_and_plot(poly_features[0:2, :], poly_features[n_train:, :], labels[0:2], labels[n_train:])"
      ],
      "execution_count": 115,
      "outputs": [
        {
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            "final epoch: train loss 0.29098982 test loss 10.967399\n",
            "weight: [[2.8922033  0.99134535 2.944083  ]] \n",
            "bias: [0.7347255]\n"
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        "id": "DYgmeMw8Ho3U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "effee170-77c2-40b7-fd2e-e4229de90645"
      },
      "source": [
        "n_train "
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtlaWTIeIJ_k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "40bf9938-7209-4861-c1e0-2135633d47d9"
      },
      "source": [
        "poly_features[0:10, :]"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[-9.7909808e-01  9.5863307e-01 -9.3859577e-01]\n",
              " [ 1.0471004e+00  1.0964193e+00  1.1480612e+00]\n",
              " [-8.5697544e-01  7.3440689e-01 -6.2936866e-01]\n",
              " [ 9.0210015e-01  8.1378466e-01  7.3411524e-01]\n",
              " [ 1.0002568e+00  1.0005137e+00  1.0007706e+00]\n",
              " [-2.2165654e+00  4.9131622e+00 -1.0890345e+01]\n",
              " [ 1.4259140e-01  2.0332308e-02  2.8992123e-03]\n",
              " [ 2.4022633e-01  5.7708688e-02  1.3863146e-02]\n",
              " [ 6.3526750e-02  4.0356480e-03  2.5637160e-04]\n",
              " [-3.9864253e-02  1.5891587e-03 -6.3350628e-05]]\n",
              "<NDArray 10x3 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOJtmPC1IRLs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "ab0f7f79-6c2c-482a-e663-98b9e3bd2cb9"
      },
      "source": [
        "features[:10, :]"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[-0.9790981 ]\n",
              " [ 1.0471004 ]\n",
              " [-0.85697544]\n",
              " [ 0.90210015]\n",
              " [ 1.0002568 ]\n",
              " [-2.2165654 ]\n",
              " [ 0.1425914 ]\n",
              " [ 0.24022633]\n",
              " [ 0.06352675]\n",
              " [-0.03986425]]\n",
              "<NDArray 10x1 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAMjDihHIjbT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# trainer.step??"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzfYUPi1Ui8J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "84f16877-adf6-41c5-dc78-25c5bcf4bbbf"
      },
      "source": [
        "net"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Dense(784 -> 256, Activation(relu))\n",
              "  (1): Dense(256 -> 10, linear)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXsMEs8tYqtw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# nn.Dense??"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}